Overview

Duration is 1 min

In this lab you will upload images to Cloud Storage and use them to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).

What you learn

In this lab, you learn how to:

• Upload a labeled dataset to Google Cloud Storage and connect it to AutoML Vision with a CSV label file.
• Train a model with AutoML Vision and evaluate its accuracy.
• Generate predictions on your trained model.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. For example:

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

You can list the active account name with this command:

gcloud auth list
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(Output)

Credentialed accounts:
 - <myaccount>@<mydomain>.com (active)content_copy

(Example output)

Credentialed accounts:
 - google1623327_student@qwiklabs.netcontent_copy

You can list the project ID with this command:

gcloud config list project
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(Output)

[core]
project = <project_ID>content_copy

(Example output)

[core]
project = qwiklabs-gcp-44776a13dea667a6content_copy

Set up AutoML Vision

AutoML Vision provides an interface for all the steps in training an image classification model and generating predictions on it. Start by enabling the Cloud AutoML API.

Open the navigation menu and and select APIs & Services > Library. In the search bar type in "Cloud AutoML API". Click on the Cloud AutoML API result and then click ENABLE.

This may take a minute. You should now be on the following page (ensure that the MANAGE button appears and API enabled is also displayed):

Next you will need to issue each of the commands that below appear in the GCP Console. In Cloud Shell paste these commands to create environment variables for your Project ID and Qwiklabs Username, replacing <QWIKLABS_USERNAME> with the user name you logged into the lab with:

export PROJECT_ID=$DEVSHELL_PROJECT_ID
export QWIKLABS_USERNAME=<QWIKLABS_USERNAME>
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Now, create a Storage Bucket for the images you will use in testing. Create one by running the following command:

gsutil mb -p $PROJECT_ID \
    -c regional    \
    -l us-central1 \
    gs://$PROJECT_ID-vcm/
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Leave your Cloud Shell window open for additional steps to follow.

Upload training images to Google Cloud Storage

In order to train a model to classify images of clouds, you need to provide labeled training data so the model can develop an understanding of the image features associated with different types of clouds. In this example your model will learn to classify three different types of clouds: cirrus, cumulus, and cumulonimbus. To use AutoML Vision you need to put your training images in Google Cloud Storage.

In the GCP console, open the Navigation menu and select Storage > Browser:

Once there, you should see the bucket from the last step.

Before you add the cloud images, create an environment variable with the name of your bucket by running the following command in Cloud Shell, replacing YOUR_BUCKET_NAME in the command below with the name of your bucket:

export BUCKET=YOUR_BUCKET_NAME
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The training images are publicly available in a Cloud Storage bucket. Use the gsutil command line utility for Cloud Storage to copy the training images into your bucket:

gsutil -m cp -r gs://cloud-training/automl-lab-clouds/* gs://$DEVSHELL_PROJECT_ID-vcm/
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When the images finish copying, click the Refresh button at the top of the Cloud Storage browser. Then click on your bucket name. You should see 3 folders of photos for each of the 3 different cloud types to be classified:

If you click on the individual image files in each folder, and then click once more when you see the URL, you can see the photos you'll be using to train your model for each type of cloud.

Create a dataset

Now that your training data is in Cloud Storage, you need a way for AutoML Vision to access it. You'll create a CSV file where each row contains a URL to a training image and the associated label for that image. This CSV file has been created for you; you just need to update it with your bucket name.

Run the following command to copy the file to your Cloud Shell instance:

gsutil cp gs://cloud-training/automl-lab-clouds/data.csv .
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Then update the CSV with the files in your project:

sed -i -e "s/placeholder/${BUCKET}/g" ./data.csv
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Now you're ready to upload this file to your Cloud Storage bucket:

gsutil cp ./data.csv gs://${BUCKET}
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Once that command completes, confirm that you see the data.csv file in your bucket. If you are unable to see the data.csv file then refresh your bucket.

Navigate to the AutoML Vision Datasets page.

At the top of the console, click + NEW DATASET.

Type "clouds" for the Dataset name.

Leave "Single-label Classification" checked.

Click CREATE DATASET to continue

On the next screen you will choose the location of your training images (the ones you uploaded in the previous step)

Choose Select a CSV file on Cloud Storage and add the file name to the URL for the file you just uploaded - gs://your-project-name-vcm/data.csv. You may also use the browse function to find the csv file. Once you see the white in green checkbox you may select CONTINUE to proceed.

It will take around 15 to 25 minutes for your images to import and be aligned with their categories. Once the import has completed you'll be returned to the "Select files to import page", click the IMAGES tab to see the images in your dataset.

Inspect images

Next proceed with a brief examination of the images.

Try filtering by different labels in the left menu (i.e. click cumulus) to review the training images:

If any images are labeled incorrectly you can click on them to switch the label or delete the image from your training set:

To see a summary of how many images you have for each label, click on LABEL STATS. You should see the following pop-out box show up on the right side of your browser. Press DONE after reviewing the list.

Train your model

You're ready to start training your model! AutoML Vision handles this for you automatically, without requiring you to write any of the model code.

To train your clouds model, go to the TRAIN tab and click START TRAINING.

Enter a name for your model, or use the default auto-generated name.

Leave Cloud hosted selected and click CONTINUE.

For the next step, type the value "8" into the Budget * box and check "Deploy model to 1 node after training." This process (auto-deploy) will make your model immediately available for predictions after testing is complete.

Click START TRAINING.

Training this dataset will take around 55 to 90 minutes to complete. The total training time includes node training time as well as infrastructure set up and tear down.

Evaluate your model

After training is complete, click on the EVALUATE tab. Here you'll see information about Precision and Recall of the model. It should resemble the following:

You can also adjust the Confidence threshold slider to see its impact.

Finally, scroll down to take a look at the Confusion matrix.

This tab provides some common machine learning metrics to evaluate your model accuracy and see where you can improve your training data. Since the focus for this lab was not on accuracy, move on to the next section about predictions section. Feel free to browse the accuracy metrics on your own.

Generate predictions

Now it's time for the most important part: generating predictions on your trained model using data it hasn't seen before.

There are a few ways to generate predictions. In this lab you'll use the UI to upload images. You'll see how your model does classifying these two images (the first is a cirrus cloud, the second is a cumulonimbus).

First, download these images to your local machine by right-clicking on each of them (Note: You may want to assign a simple name like 'Image1' and 'Image2' to assist with uploading later):

Navigate to the TEST & USE tab in the AutoML UI:

On this page you will see that the model you just trained and deployed is listed in the "Model" pick list.

Click UPLOAD IMAGES and upload the cloud sample images you just saved to your local disk (you may select both images at the same time).

When the prediction request completes you should see something like the following:

Excellent - the model classified each type of cloud correctly!

Congratulations!

You've learned how to train your own custom machine learning model and generate predictions on it through the web UI. Now you've got what it takes to train a model on your own image dataset.

What was covered

• Uploading training images to Cloud Storage and creating a CSV for AutoML Vision to find these images.

• Reviewing labels and training a model in the AutoML Vision UI.

• Generating predictions on new cloud images.

Next steps / learn more

• Watch the intro video
• Learn more about how AutoML Vision works by listening to the GCP Podcast episode
• Read the announcement blog post
• Learn how to perform each step with the API

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

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